{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "import mxnet as mx\n",
    "from mxnet import autograd, gluon, image, init, nd\n",
    "from mxnet.gluon import data as gdata, loss as gloss, utils as gutils\n",
    "import sys\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "d2l.set_figsize()\n",
    "img = image.imread('./data/train_tiny/47.png')\n",
    "d2l.plt.imshow(img.asnumpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(imgs, num_rows, num_cols, scale=2):\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    for i in range(num_rows):\n",
    "        for j in range(num_cols):\n",
    "            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())\n",
    "            axes[i][j].axes.get_xaxis().set_visible(False)\n",
    "            axes[i][j].axes.get_yaxis().set_visible(False)\n",
    "    return axes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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       " </g>\n",
       " <defs>\n",
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       "   <rect height=\"98.836364\" width=\"98.836364\" x=\"10.7\" y=\"10.7\"/>\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flipping\n",
    "num_rows = 2\n",
    "num_cols = 2\n",
    "Y = [gdata.vision.transforms.RandomFlipLeftRight()(img) for _ in range(num_rows * num_cols)]\n",
    "show_images(Y, num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a27b6e898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111503860>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a2795f710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a27f9b4a8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
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       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
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       "   <rect height=\"98.836364\" width=\"98.836364\" x=\"10.7\" y=\"10.7\"/>\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fading\n",
    "num_rows = 2\n",
    "num_cols = 2\n",
    "Y = [gdata.vision.transforms.RandomBrightness(0.5)(img) for _ in range(num_rows * num_cols)]\n",
    "show_images(Y, num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a28261908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2838af60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a283702b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a283b3940>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/svg+xml": [
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       "<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
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       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"pa9f0ae097c\">\n",
       "   <rect height=\"98.836364\" width=\"98.836364\" x=\"10.7\" y=\"10.7\"/>\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_aug = gdata.vision.transforms.RandomColorJitter(\n",
    "    brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n",
    "\n",
    "num_rows = 2\n",
    "num_cols = 2\n",
    "Y = [color_aug(img) for _ in range(num_rows * num_cols)]\n",
    "show_images(Y, num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a287a2978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a286062e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a285f9898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a28823f28>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a286c3630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a286e8c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2996b358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a29993630>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "    <image height=\"98\" id=\"imagefb2c049d1e\" transform=\"scale(1 -1)translate(0 -98)\" width=\"98\" x=\"10.7\" xlink:href=\"data:image/png;base64,\n",
       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P7d09bxsQt3f37G9u6IcBv3qCX/E+IWRGA1oI9BYdTanuJqmIJaNVIOfMPM8s88Iyz8zLSi4NiHFH0IGgA0aFZmZKstwu6gVC1At8LcBKMA0QbTS6cyhTbfcqUZpYWlfS6quYKYJqKzUPIourpZiLIMZMCJF19c1iqlZVfgVEPZVVRKZSSM3qUghyMwtKrg6dLO2UyXoixGCQ3Q6RBXKe0eMOt9uRb8YKhNIGACUNSlm0NHTdyDDs2e9vOBxuGHc7uq6j/qwg5YwPkXldEUJgcsGWl5BhaeENKTPImtFQcq4ZG+2+wxjDuBtAFFzXEddAXCJhDdXwEJIsRZXnsl7ySCnQEswGhgBpTQNC11URBWQh5cQyL9d0mjUW1gRr2k6sRGQBqVAyxJhZ18A8ryiT0SajcyaliFD11FFKvUjqOoRziFfZHLm98S2mVc2BF/8FY5HSgrLQDWhroLMwugrEuD80REz1grVlN+7p+g6lqoJd15nT+YTWqsZHfGinwiOkxIZAiBYTTNURLYwhVFWA1jqUkuyGnpISOccWDV3r9IEYEtGnGlktkBCkUneXbpZQVcagRdODgmogGIPQqloqovoSKSXWaWadF5Z54TStPE2e58lzmT0qLMggkCmTkyD4xLKsnC8T6uqU1pBJt9sxHG7QxtC5Dtf1ONchUkIETwmBlKuu0S2UI1/pi0xGGAlGVd9ISrAGylCD3oC+fXwDgNUWpy1WW4Z+YLcf0UaTcuAynck5MU0XrLWvsjQKSmusNVhnsLb6Ca5zdSpJ33WM446+czhbTb3O6irx27VrLoWYaqwpZupsV4u5gNPQaXBaNEV8tS+uVl79YrP8SiGniL/M+GnGX2Z+/PDMd++f+e7dEz9/PJLmMzlL0ppJAZYlcDpNKApIUcWhFIw3e371n/8Tj2/fMt7cIJVGSI1QmnA64Y9H/PGZtMxVxzafSshMibE6qGtG9ALZV70nlIC+ozgNpYmmu/tHAHrX0buOwfXsdiO3t7fc3R24vTvQua4utjForclZ1JBDk7FCvgT/nDOMQ8+4G+j7gaHvcdZijEGpKjML7fZNvzhrW7IWyJp2kioguYBTYDXYZhH9yyNfzfCcIkEZonWEricpS9IOYevdw3o5s17O+MsZRUJowZwi+Xy6XguXUvA581UuaNfRjbv2ZhVIRYmRHGuOVlaKFkwm1ZByy9OKlJiRItQgYykIo1o8TiGaatCPjxWI/bhjP+7Z73YcDjc8Ptzz8HjP4+MdztpX0U1FSjXGkzItxbKapTFG9vs9h8OOw2FP1/Vo7ZCq6pbYEs3mkl9SKlswUTWTWGnRvGfQub4xLet8HcT7naO8fCAQVW8gkFpzkBrRdYyHG95cZi7nM9P5zOV8Zp3O+OXMMp85nS51YVNN3/S5MM0rsVABEC8vRGqDHgaElOSug+ApfiUEX1NISyKXRBEJtYBMEeVrcFB1FuXcCxBvHt8CcHs41FNwOHB/f88XXzzyxdtH3n7xBmd0XbhmV6dYd2xKgss0cTqdOJ6OTNOF/X7H3e2Bh4dbnHOEKEgJYqpWSU3BrLmnqoFrtMI5g3Ki3TGDLlzlZ9P9yN8HxIZGqdaTMgqpNaV0HPqB4TbxGDNrCDyfzjwfTzyfznz88I6PP2eO0zOfTkeyD6SWFZiQXJaVlIEWKd1kozSmZom4jhw88Xwi5kRY5pp1WCK5RAoJlRLSC5QU6M4BAqEdSjQgvvnqawDu7m65u7vl/u6O+/tbHh/veXyoSb5SvoSec85ELVCpEJMg5IxZV4wxaF1vubrOMfQ1yrquhRCrcVeyJCdBktsdhrheEEnxstjXcNAfMlKEsNZn8ZA9lFDNZ90jdA+6q7pMtJOZMsZqrLO4oacQmaYjfFIsObKuC36aWacJZS3zshBTfvWaWsi9hcvRGiElYZmJgE+RZZ5rxuQysfr5lYEh6MaB4e6OcY0MW+7r3/3q1xWI2xvu7g5VLxz27HYju6FDyypSlnb57n0k5Zo2mDIsy8q8rsTmO7yMKmK0rs6ikmC0xVlJjLrGnMSLeDJGobX8/eLnl8MvcP4I8xnSGdKlPqWA8Q2Mj/UpLdWZU0ghsMawG/tqCc5nnsYB23cIq4mXzGVdOJ1O2K5nXhdSbhnhXGPsn72MQvVdQk6sMfLpeOTd+3e8e/8zn54+YVQNy2gpuDnc8Pj2DY9v3/KwrL8EYl/n3Z7dbqgRVaPQUuBTZlk8p/PMtPiWy1r1RE219zWU/RkM9ZLGtPhP1oJSJKXoa8b3dq8tW4aH3K5P/9BRCoQFju/h+AHCRwifIH6sSuXu70EW6HZNrtcsFSEUzhq0UvTOMU8nxl2P7RzCGjwViE+nZ0znmJeZlEJNrBPVVfnljikFUs6ElFhj4NPxxD9//wP/+E//yPc/fH+NR1ktebi/49enM2nxmLZu+v5wC8DtYcftbVWyw9h9lnyxyd2yXWtuIV8KUoLRCiUsJSu6FoMR4vOkAvWvSkz4A8HIGXJsM0Cq8Squu/jzXC3x6moVav7szc0Nt/e33B3vuRzPJAHnacYdjxxPJ06nE5fpXMP9yqC0+XzTCECpGiOzjigE52Xlw9OR73/8uYXnQUnBZZoxyjC4qrsAtNx2cqo1CjWfv8nr9veNVgydRQhB58wViFKonnJz0iiF3dgz9N31Tf5ZhxBgO7h5AOMgHiC9oTTRJHZvoX8DqgdhXqyeXwznOu7v7vExoJQmXFY+/PiOGCPH05n379/x/Q/f0w819FPvZjSvxZOUEtv1lJuM1Jr90zPD4Q437tCu3nmHWEsDXDdxnGYui2cO9RpCi9SAaOn11xhKA6NAPcKdwxpNyl1Loa+/dk1GbvZ7vZuuHulfZBgHN48w3ED2lBygrCAExQwIM4DsGwDb1vp8dM5xf3ePtoZx3PHhx3f8Y///EUIihhPv3r/nhx++ZxgHHh8iSmvGcf+ZdBJSYroOqTSuH9h9emI43OLGPdp1+JRq1t9lwjrHcVo4L57JNyBM27lKypqJR42bvNxhtfRFJflXZd/8uYe2dcJ2RcZLaL8t/O84Ba+HMYab3Z6u7xn6kceHR/a7PcZaoq9VVJd5YpouLLuba1b861Hzqyy6hfz3t/fcPrzh/u0XPHz8iPr0iYxg8YEiNVmoej/Sbq317lBjTf3Y0VlX76FpZiR/uBX572KImr2x3U2La0rM7/s1US03NJ213Oz3PD4+8M2335BS5OtvvuHLL7/i4fENNzc3Nc70e/7uuNvx9bffUkrh9vaWdz//zLuff+Ldzz+z6zu+/vYbHt68ZXfbYn0bEK43OFcdFCU2q+d/tdEuc/7IVy6FAFlzZItpQLx55Otvv6aUwlfffM0XX33F45s3DP0O1/1+IHa7Hd98+ysOh1u++dWv+P677/jNd9/x/XffYZXkqy8eeXjzhn0zlvS43wFgrETbeuv1F1Czf8bxx28fIWrCAUAxhpv9DW/fvuXXf/drEIKvvvmGt198we3dA0YbjDa/97/ph4F+GHj7xReEEDgcbtntbxjHHaTI/e2eu8MN/W4PgLauHmOlW+LuX0jH/nsdUkhub2/5u1/9XUuoE3z95dfsxz22ZXZIIf+oUyeFYLfb8fbtW5SUpOhrMaVR18DpFQghRb26/aNd2/+9hpCSu8MtSkru7u4AeLi/ZzfuMNo25/OP261CSsb9HqkUu/2esC4kv5L9Smq5r6L8e6jD+g821mni9PyJ89Mnzs9PwL9Le/Q/wBC1UEdrc81w+RsQf4WxJW9rpTGm3Vn/lV/Tf8ixpbTqzQIDdDnO9ZuqZWeZlsLyt/FnGxsQUinUVTQ9TQCUrqZ3CGngbzj82UetJazZ8wC6PLcTkWoOElaB+V/bpft3P5oJLNUrIL7/H/8VAHt3g/MHXLnBlBGhasqIkOoPitf8bfzhI6XMvCxcTpX7BED/X//n/wHA/ddfc//NN9z7b9g/PCK7EdUNqG78D+/k/amHD56np2fevfuZdz+/A0D/5jf1RASxgEloJ5CyoMcVnRKaVqsgVWMH+JsC+beOFBPzunA8nfn46RMAOoqqI47TO/LPiVN4Yvjwz9jdHXa8x+zuGPcHdvsDu/0t/TD+Nd/D/x7jWpFVy8MAdJQvQJzCE/LTd+huxO0e6HaPuN0jD2+/5u2X36KN/RsQf4KxOXRSadSmrKNYAFimZxbvmf1KFoZ+95Zu94Z+95Z5nlFKs7u55eZwd81FEv/RQ7X/ynF16IzB2uZZr9EDMPuVaVmY5oU1CT6dBUWvFH3i56Pn3fPMjx+f+Prb31SWydt7bu/ur7GSv40/fGij2e12xJTQutVZr40LYg6ey7xwvkxclswUPHM8MYX3/PDhxE8fn/j+3Y988+N3/P3f/xf+09//F8bd/m9A/CuG0ZrdbkS3J4BeUz0Ri1+5LDOnaeL55Plwgg8nwccTHH56z29+/om3P/wPfvXTd8QY2e32fPvNr4G/6Yw/dpiWVb/bjdckBO2GejRCMSTRgSoIYxAuo/tCNxT6neBmF7D6Qk4fOZ//mXc/7/nv/yQ5Pbyl7/f0/R7XjbUE7BUbzJ9i1NyMFw618ovvXb/eat1LFp+X9l4ZbRohZPu3Md1sWYkbS0GtFBUYVdNC/xxj8822p7a7CsSgHMKAtgrbB7pdZpwTt0tGWY3rM65bEDwxXb7j558FRp85Xb7h/uEb7u+/RluFFAYpNALzJ/HINxByqXUHrV6wfa8u45bLl0vNUK90Q1vpQGWWrPVvlWc2l1pTuuVkbSwGinp3PVjNaHXNzVV/GWdWd2M1n6QWaKtwnaH3kdEnDo03tQBFZYpckCSmi+Tnny94/yPT8p5UVmxn2R9uUVfWYP0nywLZij8ipdWqQRHlClJqYKQsiLkQt1KAWIipzZxIORJzTZcv5YWjVgpQVMYyIyQpl8pOZv5yVqHOouqIIhOoiNARVTJOFpSWWNfeXKnFiBCI/sJ0pqbAZ0lYC89PF374/ieM3WHtHmt3KNO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      ],
      "text/plain": [
       "<Figure size 576x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_aug = gdata.vision.transforms.RandomColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)\n",
    "shape_aug = gdata.vision.transforms.RandomResizedCrop((200, 200), scale=(0.1, 1), ratio=(0.5, 2))\n",
    "\n",
    "multi_operation = gdata.vision.transforms.Compose([\n",
    "    gdata.vision.transforms.RandomFlipLeftRight(), color_aug, shape_aug])\n",
    "num_rows = 2\n",
    "num_cols = 4\n",
    "Y = [multi_operation(img) for _ in range(num_rows * num_cols)]\n",
    "show_images(Y, num_rows, num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_operation = gdata.vision.transforms.Compose([\n",
    "    gdata.vision.transforms.ToTensor(),\n",
    "    gdata.vision.transforms.Normalize(mean = (0.5, 0.5, 0.5), std = (0.5, 0.5, 0.5))\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML_Final_Project",
   "language": "python",
   "name": "ml_final_project"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
